How HoneyHive is making Agents work at enterprise scale

Most AI systems, especially large language models (LLMs) or Agents, can behave in unpredictable ways. They may hallucinate, make hidden reasoning errors, or fail without a clear explanation.

Traditional software analytics tools aren’t capable of detecting subtle failures, so while building LLM prototypes is relatively straightforward, scaling them into robust, enterprise-grade applications is challenging.

Even promising AI applications fail in unexpected ways when deployed, and systematic evaluation is manual and time-consuming, making it hard to figure out what happened and how to improve. As a result, an estimated 52% of AI Agent prototypes fail to transition into production due to performance and debugging challenges.

As AI Agents become ubiquitous and enterprises increasingly depend on them for mission-critical workflows such as writing, coding, and summarizing, it’s crucial to understand why they make the decisions they do.

This was the inspiration for HoneyHive AI, a platform that helps companies monitor, evaluate, and improve their AI systems. It gives teams deep visibility into how their AI is thinking and performing, spots problems like hallucinations or retrieval errors, and provides tools to test fixes and measure improvements — so companies can confidently deploy AI in real-world environments.

A trust layer for the agentic era

HoneyHive founders Dhruv Singh (CTO) and Mohak Sharma (CEO) met as roommates at Columbia University and share firsthand experience of the unpredictability of AI.

Sharma worked as a product manager at Templafy, building out data and AI platforms, while Singh gained an early insight into LLMs as a software engineer on the OpenAI Innovation team at Microsoft.

“2021 … was a very interesting time at Microsoft, because no one outside Microsoft knew about LLMs,” he recalls. “I remember … thinking, oh my god, this is going to be everywhere in the future.”

But the Microsoft team kept running into the same challenges, says Singh. “How do we evaluate this AI? It’s doing such weird things. How do we make sure it’s doing it reliably and repeatably?”

The opportunity was clear — very soon, teams would need help to build and scale their own systems. “I realized that it would be very important to start a company around ensuring that people are able to take these models to production successfully.”

“The biggest point of friction with working with Agents is not knowing whether or not you can trust the output.”

HoneyHive launched in October 2022, with a platform that promised to help developers turn their LLM prototypes into production-ready AI products.

“I think the simplest problem that we solve for our customers is telling them what their Agents are doing and why,” says Singh. “It’s very hard for teams to be able to quickly catch and fix what is exactly going wrong in their AI’s thinking … We help people know what’s going wrong and what they need to focus on fixing.”

Unlike traditional observability tools, HoneyHive tracks every step of an AI pipeline with custom metrics, handles large volumes of AI data, integrates with enterprise systems through OpenTelemetry, and offers secure deployment options for full traceability and auditability.

“We have extremely robust experimentation tooling,” explains Singh. “You catch an issue in production, you create an evaluation around it, and then keep improving on it. That’s the end-to-end workflow that we enable for companies.”

From beta to boardroom

During its beta period, HoneyHive built an impressive customer roster of AI startups and Fortune 100 enterprises and dramatically improved how teams build and deploy AI products.

One customer improved their Agent accuracy on web-browsing tasks by 340% within a few months of implementing HoneyHive. Another accelerated their development cycle fivefold because they could confidently ship new AI Agents.

HoneyHive reached general availability in April 2025, having doubled its team size and seen more than 50x growth in AI requests logged through its platform in 2024 alone. The announcement came alongside a $5.5M seed round led by Insight Partners, with HoneyHive welcoming Insight Managing Director George Mathew to its board of directors.

“Enterprise AI Agents are evolving from performing simple tasks to becoming the building blocks of sophisticated AI systems,” Mathew said at the time.

“HoneyHive’s approach of leveraging traces for evaluations and monitoring within multi-Agent architectures plays a critical role in the enterprise AI stack. The team’s awesome execution and deep technical expertise position us well in this segment of the observability market.”

Scaling with AI

HoneyHive is using AI across every part of its workflow, from coding and customer research to internal support, to enable each team member to do the work of five people, Singh explains. Despite being a team of just eight, they operate at the scale of a much larger company by using AI throughout their own workflows — from coding and customer research to internal support.

“Pre-AI, we could not achieve the level of success that we have with the team that we have. It’s only possible because we’re using AI in every single place we can.” Singh estimates that AI enables each team member to do the work of five people.

“Every time we work with a large enterprise, they think, ‘Oh, you must be a 50 to 100 person team’,” he says. “We’re actually just eight.” 

The key, he explains, is how deliberately they’ve trained their own Agents. “We spend an incredible amount of time documenting things, because we know that Agents are only as good as your documentation.”

Singh and his team are already looking ahead to the next evolution of their platform, which is the creation of what Singh calls a “meta Agent”: An AI system designed to help people build, evaluate, and improve other Agents. “Agents are good at writing code now, and they’re good at processing a lot of text. If you think about what’s involved in building an Agent, it’s writing code and then reading the logs to figure out how to improve the code.”

Building for the future of work

For HoneyHive’s next product phase, there is essential groundwork that needs to be in place. The company is expanding its observability platform and integrations, and developing advanced evaluation tools that can simulate and stress-test Agent behavior — the essential infrastructure for creating and managing “meta Agents” safely.

Singh calls this the “oversight problem”: As Agents grow more capable, their reasoning will outpace human ability to monitor them. “Agents will be putting out a textbook’s worth of reasoning just to answer one question,” he says. “Who’s going to read that?”

HoneyHive aims to ensure that, even as AI systems evolve, humans never lose visibility into how they work.

By addressing both scalability and accountability, HoneyHive is positioning itself as the trust and observability layer for the agentic era — enabling teams to build more powerful AI responsibly.

*Note: Insight has invested in HoneyHive and Templafy.

This article is part of our ScaleUp:AI 2025 Partner Series, highlighting insights from the companies and leaders shaping the future of AI.